Increasing evidence suggests that electrical stimulation may be efficacious for Alzheimer's disease (AD); however, mechanistic explanations in support of the potential effect of stimulating electric fields for the treatment of AD are almost completely lacking. Importantly, there are currently no multiscale computational platforms and connectomic models to use to predict the current spread in anatomically correct neural tissue and potential electrical damage to it due to DBS electrodes. This lack of predicting capabilities significantly hinders the further development of this technology, which is primarily subjected to empirical observations guiding the search for answers. The goal of the proposed effort is to develop and make available to the scientific community a modular, integrated, multiscale computational modeling framework, informed by and verified through in-vivo experimental studies, that facilitates the design of safe and effective deep brain stimulators (DBS) for AD. This computational platform comprises global models of the extracellular media, including multi-electrode arrays and neurostimulators in general, as well as neuron modeling, which will provide the basis for emerging predictions of safe CNS neurostimulation. The proposed effort leverages the computational modules and model discretization strategies being developed in a parent R01 dedicated to the development of predcitvive multiscale computational methods for the stimulation of the peripheral nervous system. The proposed supplement begins the much needed development of both models and paired computational platform toward the goal of offering to the AD research community a versatile and modular software package to address the uncertainties associated with DBS of forniceal and hippocampal tissue. This project is the beginning of the roadmap to develop predictive tools that can be used by the community to treat AD with DBS.